Research on the Application of Instance Segmentation Algorithm in the Counting of Metro Waiting Population

  • Yan Cang
  • Chan ChenEmail author
  • Yulong Qiao
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1107)


With the development of deep learning, intelligent security and measurement and control products emerge in endlessly. In order to maintain the operation order of subway station, it is one of the important problems in the field of deep learning to carry out real-time and accurate counting of the number of people queued in front of the subway. In order to solve this problem, we propose a simple and flexible real-time counting method for the number of people queued in front of the subway. The image is collected by the camera in front of the subway door, instance segmentation algorithm accurately divides the target in the image, and completes the counting of the number of people queued in front of the subway by calculating the number of targets segmented. Selected the mainstream Mask R-CNN as the basic algorithm, combine the characteristics of the live picture, feature pyramid network and non-maximum suppression process of Mask R-CNN are improved. The experimental results show that the algorithm can realize accurate and real-time counting of the number of people queued in front of the subway, and the accuracy of counting can reach 96%. Compared with the traditional target detection algorithm, it has stronger adaptability to occlusion problem, and can accurately segment the intersection of targets from less than 30% to less than 60%. And the real-time performance has been greatly improved, the target inventory in a single picture only needs about 0.2 s.


Instance segmentation Target counting Deep learning Feature pyramid network 


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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Harbin Engineering UniversityHarbinChina

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